PDF(Pubmed)
Abstract：
In tactile sensing, decoding the journey from afferent tactile signals to efferent motor commands is a significant challenge primarily due to the difficulty in capturing population-level afferent nerve signals during active touch. This study integrates a finite element hand model with a neural dynamic model by using microneurography data to predict neural responses based on contact biomechanics and membrane transduction dynamics. This research focuses specifically on tactile sensation and its direct translation into motor actions. Evaluations of muscle synergy during in -vivo experiments revealed transduction functions linking tactile signals and muscle activation. These functions suggest similar sensorimotor strategies for grasping influenced by object size and weight. The decoded transduction mechanism was validated by restoring human-like sensorimotor performance on a tendon-driven biomimetic hand. This research advances our understanding of translating tactile sensation into motor actions, offering valuable insights into prosthetic design, robotics, and the development of next-generation prosthetics with neuromorphic tactile feedback.
摘要：
在触觉传感中，解码从传入触觉信号到传出运动命令的旅程是一个重大挑战，主要是由于在主动触摸过程中难以捕获群体级传入神经信号。这项研究通过使用微神经成像数据将有限元手模型与神经动力学模型集成在一起，以基于接触生物力学和膜转导动力学来预测神经反应。这项研究特别关注触觉及其直接转化为运动动作。在体内实验期间对肌肉协同作用的评估揭示了连接触觉信号和肌肉激活的转导功能。这些功能提出了类似的感觉运动策略，用于受物体大小和重量影响的抓握。通过在肌腱驱动的仿生手上恢复类似人的感觉运动性能来验证解码的转导机制。这项研究促进了我们对将触觉转化为运动动作的理解，为假肢设计提供有价值的见解，机器人,以及具有神经形态触觉反馈的下一代假肢的开发。
